Sustained Online Amplification of COVID-19 Elites in the United States

The ongoing, fluid nature of the COVID-19 pandemic requires individuals to regularly seek information about best health practices, local community spreading, and public health guidelines. In the absence of a unified response to the pandemic in the United States and clear, consistent directives from federal and local officials, people have used social media to collectively crowdsource COVID-19 elites, a small set of trusted COVID-19 information sources. We take a census of COVID-19 crowdsourced elites in the United States who have received sustained attention on Twitter during the pandemic. Using a mixed methods approach with a panel of Twitter users linked to public U.S. voter registration records, we find that journalists, media outlets, and political accounts have been consistently amplified around COVID-19, while epidemiologists, public health officials, and medical professionals make up only a small portion of all COVID-19 elites on Twitter. We show that COVID-19 elites vary considerably across demographic groups, and that there are notable racial, geographic, and political similarities and disparities between various groups and the demographics of their elites. With this variation in mind, we discuss the potential for using the disproportionate online voice of crowdsourced COVID-19 elites to equitably promote timely public health information and mitigate rampant misinformation.

[1]  Nastaran Pourebrahim,et al.  Understanding communication dynamics on Twitter during natural disasters: A case study of Hurricane Sandy , 2019, International Journal of Disaster Risk Reduction.

[2]  M. Hindman The Myth of Digital Democracy , 2008 .

[3]  Sarah J. Jackson,et al.  #HashtagActivism: Networks of Race and Gender Justice , 2020 .

[4]  E. Wrigley-Field US racial inequality may be as deadly as COVID-19 , 2020, Proceedings of the National Academy of Sciences.

[5]  K. Fink The biggest challenge facing journalism: A lack of trust , 2019, Journalism.

[6]  J. E. Hirsch,et al.  An index to quantify an individual's scientific research output , 2005, Proc. Natl. Acad. Sci. USA.

[7]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[8]  Zizi Papacharissi,et al.  Networked Gatekeeping and Networked Framing on #Egypt , 2013 .

[9]  Maria de Fatima Oliveira,et al.  Affective News and Networked Publics: The Rhythms of News Storytelling on #Egypt , 2012 .

[10]  T. Valente,et al.  Accelerating the Diffusion of Innovations Using Opinion Leaders , 1999 .

[11]  D. Lazer,et al.  Fake news on Twitter during the 2016 U.S. presidential election , 2019, Science.

[12]  Giovanni Colavizza,et al.  Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis , 2020, ArXiv.

[13]  Nahema Marchal,et al.  “Coronavirus EXPLAINED”: YouTube, COVID-19, and the Socio-Technical Mediation of Expertise , 2020, Social media + society.

[14]  Kristina Lerman,et al.  Political polarization drives online conversations about COVID‐19 in the United States , 2020, Human behavior and emerging technologies.

[15]  Jacob Ratkiewicz,et al.  Political Polarization on Twitter , 2011, ICWSM.

[16]  Allan St John Holt Assessing the Risks , 2008 .

[17]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[18]  Richard A. Grusin Premediation: Affect and Mediality After 9/11 , 2010 .

[19]  Joan Donovan,et al.  Social-media companies must flatten the curve of misinformation. , 2020, Nature.

[20]  Sarah J. Jackson,et al.  #Ferguson is everywhere: initiators in emerging counterpublic networks , 2016 .

[21]  H. Park,et al.  Predicting Opinion Leaders in Twitter Activism Networks , 2014 .

[22]  Duncan J. Watts,et al.  Who says what to whom on twitter , 2011, WWW.

[23]  A. Chadwick The Political Information Cycle in a Hybrid News System: The British Prime Minister and the “Bullygate” Affair , 2011 .

[24]  Robert Gorwa,et al.  Unpacking the Social Media Bot: A Typology to Guide Research and Policy , 2018, Policy & Internet.

[25]  Kristina Lerman,et al.  Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set , 2020, JMIR public health and surveillance.

[26]  M. Williams,et al.  Linking Survey and Twitter Data: Informed Consent, Disclosure, Security, and Archiving , 2019, Journal of empirical research on human research ethics : JERHRE.

[27]  Brooke Foucault Welles,et al.  The Battle for #Baltimore: Networked Counterpublics and the Contested Framing of Urban Unrest , 2019 .

[28]  Brandi Collins-Dexter Canaries in the Coalmine: COVID-19 Misinformation and Black Communities , 2020 .

[29]  Kwan-Liu Ma,et al.  Breaking news on twitter , 2012, CHI.

[30]  Manlio De Domenico,et al.  Assessing the risks of 'infodemics' in response to COVID-19 epidemics. , 2020, Nature human behaviour.

[31]  Brooke Foucault Welles,et al.  On minorities and outliers: The case for making Big Data small , 2014, Big Data Soc..

[32]  Kyu S. Hahn,et al.  Red Media, Blue Media: Evidence of Ideological Selectivity in Media Use , 2009 .

[33]  Casey Fiesler,et al.  “Participant” Perceptions of Twitter Research Ethics , 2018 .

[34]  C. M. Danforth,et al.  Allotaxonometry and rank-turbulence divergence: A universal instrument for comparing complex systems. , 2020, 2002.09770.

[35]  Zizi Papacharissi,et al.  The Citizen is the Message: Alternative Modes of Civic Engagement , 2009 .

[36]  Sharon Meraz The Many Faced “You” of Social Media , 2009 .

[37]  Kristen M J Azar,et al.  Disparities In Outcomes Among COVID-19 Patients In A Large Health Care System In California. , 2020, Health affairs.

[38]  P. Howard Reply to Evgeny Morozov's review of The Digital Origins of Dictatorship and Democracy: Information Technology and Political Islam , 2010, Perspectives on Politics.

[39]  Skyler J. Cranmer,et al.  Elusive consensus: Polarization in elite communication on the COVID-19 pandemic , 2020, Science Advances.

[40]  Filippo Menczer,et al.  The rise of social bots , 2014, Commun. ACM.